对高职院校开设数学建模课程的认识与实践

数学模型指通过抽象和简化,使用数学语言对实际事物的一个近似的描述,以便于人们更深刻地认识研究对象;数学建模培养学生的观察力、想象力、抽象力和创造力,从而提高学生综合素质;数学建模的课程主要通过启发与讨论相结合、理论与实践相结合的教学方式进行。     

视觉新语言的培养——设计素描教学探索

设计素描教学除了培养学生的造型能力外,更重要的在于训练学生用设计的思维去看待和理解客观对象。只有从设计的理念出发去把握对象并进行新的组合,才能设计出具有创意的作品。     

Adaptively selecting biology questions generated from a semantic network

The paper describes a biology tutoring system with adaptive question selection. Questions were selected for presentation to the student based on their utilities, which were estimated from the chance that the student’s competence would increase if the questions were asked. Competence was represented by the probability of mastery of a set of biology knowledge components. Tasks were represented and selected based on which knowledge components they addressed. Unlike earlier work, where the knowledge components and their relationships to the questions were defined by domain experts, this project demonstrated that the knowledge components, questions and their relationships could all be generated from a semantic network. An experiment found that students using our adaptive question selection had reliably larger learning gains than students who received questions in a mal-adaptive order.     

More student trust,more self-regulation strategy? Exploring the effects of self-regulatory climate on self-regulated learning

Self-regulated learning has been one of the important areas in educational research. The authors adopted structural equation modeling to explore and compare the impacts of three aspects of self-regulatory climate (i.e., academic emphasis, teacher trust, and student trust) on three features of self-regulated learning (i.e., self-efficacy, intrinsic motive, and self-regulation strategy). The results revealed both direct effects of academic emphasis on students’ use of self-regulation strategy, and indirect effects mediated by self-efficacy and intrinsic motive. Teacher trust has a positive impact on self-efficacy. While student trust has a positive impact on intrinsic motive, its relationship with self-regulation strategy is negative. Significant differences in school levels and gender were identified. The findings indicate that students in different cultures may have different expectations for teachers’ support in learning, which in turn influence the relationship between student trust in teachers and the use of self-regulation strategy. Implications for cultivating self-regulated learners are discussed in the article.     

Comparing Exploratory Structural Equation Modeling and Existing Approaches for Multiple Regression with Latent Variables

Exploratory structural equation modeling (ESEM) is an approach for analysis of latent variables using exploratory factor analysis to evaluate the measurement model. This study compared ESEM with two dominant approaches for multiple regression with latent variables, structural equation modeling (SEM) and manifest regression analysis (MRA). Main findings included: (1) ESEM in general provided the least biased estimation of the regression coefficients; SEM was more biased than MRA given large cross-factor loadings. (2) MRA produced the most precise estimation, followed by ESEM and then SEM. (3) SEM was the least powerful in the significance tests; statistical power was lower for ESEM than MRA with relatively small target-factor loadings, but higher for ESEM than MRA with relatively large target-factor loadings. (4) ESEM showed difficulties in convergence and occasionally created an inflated type I error rate under some conditions. ESEM is recommended when non-ignorable cross-factor loadings exist.     

Gender equity in the academic labor market: an analysis of academic disciplines

This study uses hierarchical linear modeling (HLM) to analyze the effect of human capital, structural characteristics of the discipline, and disciplinary labor market conditions on faculty salaries. Faculty in disciplines characterized by relatively low demand, high teaching loads, and low amounts of research funding earn less than do faculty in other disciplines. Additionally, even after controlling for an array of individual and disciplinary characteristics, women faculty members earn less than their male peers.     

面向项目管理的企业过程建模方法和支撑环境

1IntroductionWith internationalization of market competition,en-terprises are facing a strong competition pressure .Therefore enterprises have to make comprehensivei m-provement over ti me ,quality,cost and service by us-ing advanced technology such as concurrent engineer-ing,agile manufacturing,business process reengineer-ing, and so on. To achieve this goal , enterprisesshould i mplement the complete integration and theunitary opti mization for process technology and man-agement technology t…     

关于高师院校"数学建模"教材建设的思考与探索

我国高师院校在“数学建模”课程开设中存在的主要问题有:缺乏符合高师实际、具有高师特色的“数学建模”教材;高师院校尚未建立一套科学、有效的“数学建模”教学方法.在“数学建模”教材的构建方法上应注意:材料的来源应以借鉴、改造、探究为主;材料的选取应充分考虑中学数学教育实际与高师数学课程设置与开设的特点并充分考虑中学各基础教育学科的特点等;材料的编排应以数学建模方法为主线,采取循序渐进的编排方式,对数学建模问题的阐述应采用分步骤展开的方式.教材体系可按以下4个板块展开:绪论;数学建模方法概论;具体的数学建模方法;中学数学建模教学.     

Comparison of Frequentist and Bayesian Regularization in Structural Equation Modeling

Research in regularization, as applied to structural equation modeling (SEM), remains in its infancy. Specifically, very little work has compared regularization approaches across both frequentist and Bayesian estimation. The purpose of this study was to address just that, demonstrating both similarity and distinction across estimation frameworks, while specifically highlighting more recent developments in Bayesian regularization. This is accomplished through the use of two empirical examples that demonstrate both ridge and lasso approaches across both frequentist and Bayesian estimation, along with detail regarding software implementation. We conclude with a discussion of future research, advocating for increased evaluation and synthesis across both Bayesian and frequentist frameworks.     

Propensity Score Analysis of Complex Survey Data with Structural Equation Modeling: A Tutorial with Mplus

Propensity score (PS) analysis aims to reduce bias in treatment effect estimates obtained from observational studies, which may occur due to non-random differences between treated and untreated groups with respect to covariates related to the outcome. We demonstrate how to use structural equation modeling (SEM) for PS analysis to remove selection bias due to latent covariates and estimate treatment effects on latent outcomes. Following the discussion of the design and analysis stages of PS analysis with SEM, an example is presented which uses the Mplus software to analyze data from the 1999 School and Staffing Survey (SASS) and 2000 Teacher Follow-up Survey (TFS) to estimate the effects teacher’s participation in a network of teachers on the teacher’s perception of workload manageability.